(1) Industrial Application Field
The present invention relates to a learning control apparatus for the feedback control of quantities of objective control factors such as an air-fuel ratio of a sucked airfuel mixture, a fuel injection quantity, an ignition timing, an idle revolution speed, a quantity of sucked auxiliary air, a supercharge pressure of air supplied to an engine from a supercharger, a self-diagnosis factor and an expectation factor in an internal combustion engine.
(2) Description of the Related Art
As the known learning control apparatus for an internal combustion engine, there can be mentioned apparatuses disclosed in U.S. Pat. Nos. 4,615,619, 4,655,188, 4,729,359 and 4,715,344 and U.S. patent application Nos. 97,682 (1987) and 98,038 (1987).
In these apparatuses, a basic control quantity set based on a target value of an objective control factor such as an air-fuel ratio according to the driving state of an engine is corrected and computed by a feedback correction value set by proportion control or integration control while comparing the actual value with the target value, and the objective control factor such as the air-fuel ratio is feedback-controlled to the target value based on this control quantity. The deviation of the feedback correction value from the reference value during the feedback control is learned for each area of the engine driving state to determine a learning value for each area, and in computing the control quantity, the basic control quantity is corrected by the learning value for each area and is computed without correction by the feedback correction value. In short, the control quantity computed without the feedback control is made equal to the target value. During the feedback control, the control quantity is computed by further correcting the so-obtained value by the feedback correction value.
According to this control system, during the feedback control, the follow-up delay of the feedback control at the transitional driving can be reduced, and at the stoppage of the feedback control, a desired control output can be obtained precisely.
Accordingly, deviations of constituent parts such as an electronically controlled fuel injection apparatus can be absorbed, and the change of a filling efficiency of the engine with the lapse of time and the changes of environmental conditions such as the atmospheric pressure, the temperature and the humidity can be corrected and the highest performance of the engine can be maintained over a long period.
In these conventional apparatuses, however, there is adopted a so-called repeated learning system using a data map, that is, a system in which data map lattice sections are set according to driving states of the engine, the feedback control deviation quantity is repeatedly renewed based on the learning experience for each learning area. Accordingly, in order to increase the learning correction precision, very fine learning area sections should be set, and hence, the renewal speed is inevitably reduced. In short, the learning correction precision and the learning speed are conditions contradictory to each other.